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• W3011567438 abstract "Diabetes is one of the most challenging global health problems, affecting more than 400 million people (1). Although 10% of current global health care spending is devoted to diabetes (1), patients with diabetes remain at high risk of morbidity and mortality (2) mainly due to cardiovascular disease (3). This extremely heavy burden is likely to increase in the coming decades, especially considering the epidemic nature of the most common form, type 2 diabetes (4). It is, therefore, mandatory to address vigorously the negative impact of type 2 diabetes on vascular health and life expectancy. To this end, the availability of well-performing risk prediction models capable of identifying high-risk patients to be targeted with the most aggressive and most burdensome prevention strategies would play a pivotal role.The study of Aminian et al. (5), published in this issue of Diabetes Care , presents several models (called Individualized Diabetes Complications [IDC] Risk Scores) able to estimate in obese patients with type 2 diabetes the risk of mortality and of long-term vascular complications, including coronary artery events, heart failure, and estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Twenty-six baseline variables as potential predictors were modeled by time-to-event regression and random forest machine learning, an ensemble of survival regression trees grown on bootstrap resampling of the observations. The left-out data were then used to predict the error rate and, after permutation, to estimate the importance of a given predictor. In addition to the time-dependent area under the receiver operating characteristic curve (AUROC) and the calibration plot, a recently described index of prediction accuracy, which combines discrimination and calibration in a single value, was also used (6).A total of 13,722 patients from the Cleveland Clinic database were analyzed retrospectively (i.e., 2,287 who underwent metabolic surgery and 11,435 propensity-matched nonsurgical individuals, …" @default.
• W3011567438 created "2020-03-23" @default.
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• W3011567438 date "2020-03-12" @default.
• W3011567438 modified "2023-10-02" @default.
• W3011567438 title "Moving Toward the Implementation of Precision Medicine Needs Highly Discriminatory, Validated, Inexpensive, and Easy-to-Use Prediction Models" @default.
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• W3011567438 doi "https://doi.org/10.2337/dci19-0079" @default.
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